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Common correlated effects estimation of heterogeneous dynamic panel quantile regression models

Author

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  • Matthew Harding
  • Carlos Lamarche
  • M. Hashem Pesaran

Abstract

This paper proposes a quantile regression estimator for a heterogeneous panel model with lagged dependent variables and interactive effects. The paper adopts the Common Correlated Effects (CCE) approach proposed in the literature and demonstrates that the extension to the estimation of dynamic quantile regression models is feasible under similar conditions to the ones used in the literature. The new quantile regression estimator is shown to be consistent and its asymptotic distribution is derived. Monte Carlo studies are carried out to study the small sample behavior of the proposed approach. The evidence shows that the estimator can significantly improve on the performance of existing estimators as long as the time series dimension of the panel is large. We present an application to the evaluation of Time‐of‐Use pricing using a large randomized control trial.

Suggested Citation

  • Matthew Harding & Carlos Lamarche & M. Hashem Pesaran, 2020. "Common correlated effects estimation of heterogeneous dynamic panel quantile regression models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(3), pages 294-314, April.
    • Handle: RePEc:wly:japmet:v:35:y:2020:i:3:p:294-314
    DOI: 10.1002/jae.2753
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    Cited by:

    1. Demetrescu, Matei & Hosseinkouchack, Mehdi & Rodrigues, Paulo M. M., 2023. "Tests of no cross-sectional error dependence in panel quantile regressions," Ruhr Economic Papers 1041, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    2. Novelli, Giacomo, "undated". "Energy Dependency and Long-Run Growth," FEEM Working Papers 329650, Fondazione Eni Enrico Mattei (FEEM).
    3. Asad Nisar & Rabia Rafique, 2025. "Fiscal strategies for sustainable debt management in developing economies: dynamic common correlated effects approach," Economic Change and Restructuring, Springer, vol. 58(4), pages 1-31, August.
    4. Artūras Juodis, 2022. "A regularization approach to common correlated effects estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 788-810, June.
    5. Gagan Deep Sharma & Muhammad Ibrahim Shah & Ritika Chopra & Amar Rao & Umer Shahzad, 2025. "Impact of technological advancement and greener energy on sustainable agriculture in Asia: Evidence from selected Asian countries," Sustainable Development, John Wiley & Sons, Ltd., vol. 33(1), pages 221-237, February.
    6. Gkillas, Konstantinos & Konstantatos, Christoforos & Papathanasiou, Spyros & Wohar, Mark, 2023. "Estimation of value at risk for copper," Journal of Commodity Markets, Elsevier, vol. 32(C).
    7. Giacomo Novelli, 2022. "Energy Dependency and Long-Run Growth," Working Papers 2022.42, Fondazione Eni Enrico Mattei.
    8. Jia Chen Author-Name-First: Jia & Yongcheol Shin & Chaowen Zheng, 2023. "Dynamic Quantile Panel Data Models with Interactive Effects," Economics Discussion Papers em-dp2023-06, Department of Economics, University of Reading.
    9. Abdullah Emre Caglar & Bulent Guloglu & Ayfer Gedikli, 2022. "Moving towards sustainable environmental development for BRICS: Investigating the asymmetric effect of natural resources on CO2," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(5), pages 1313-1325, October.
    10. Razzaq, Asif & Ajaz, Tahseen & Li, Jing Claire & Irfan, Muhammad & Suksatan, Wanich, 2021. "Investigating the asymmetric linkages between infrastructure development, green innovation, and consumption-based material footprint: Novel empirical estimations from highly resource-consuming economi," Resources Policy, Elsevier, vol. 74(C).
    11. De Vos, Ignace & Everaert, Gerdie & Sarafidis, Vasilis, 2021. "A method for evaluating the rank condition for CCE estimators," MPRA Paper 112305, University Library of Munich, Germany, revised 09 Mar 2022.
    12. Ando, Tomohiro & Li, Kunpeng & Lu, Lina, 2023. "A spatial panel quantile model with unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 232(1), pages 191-213.
    13. Paulo M.M. Rodrigues & Matei Demetrescu, 2022. "Cross-Sectional Error Dependence in Panel Quantile Regressions," Working Papers w202213, Banco de Portugal, Economics and Research Department.
    14. Ngesisa Magida & Thobeka Ncanywa & Kin Sibanda & Abiola John Asaleye, 2025. "Human Capital Development and Public Health Expenditure: Assessing the Long-Term Sustainability of Economic Development Models," Social Sciences, MDPI, vol. 14(6), pages 1-27, June.
    15. Bataka, Hodabalo, 2021. "Globalization and Environmental Pollution in Sub-Saharan Africa," African Journal of Economic Review, African Journal of Economic Review, vol. 9(01), January.
    16. Shittu, Ibrahim & Saqib, Abdul & Tang, Chor Foon & Chen, You, 2025. "Does global geopolitical risk jeopardize the clean energy transition? New evidence from a global panel," Renewable and Sustainable Energy Reviews, Elsevier, vol. 223(C).
    17. Gomez-Gonzalez, Jose E. & Uribe, Jorge M. & Valencia, Oscar M., 2025. "Asymmetric sovereign risk: Implications for climate change preparation," World Development, Elsevier, vol. 188(C).
    18. Chuliá, Helena & Garrón, Ignacio & Uribe, Jorge M., 2024. "Vulnerable funding in the global economy," Journal of Banking & Finance, Elsevier, vol. 169(C).
    19. Ando, Tomohiro & Bai, Jushan & Li, Kunpeng & Song, Yong, 2025. "Bayesian inference for dynamic spatial quantile models with interactive effects," MPRA Paper 123815, University Library of Munich, Germany.
    20. David Powell, 2022. "Quantile regression with nonadditive fixed effects," Empirical Economics, Springer, vol. 63(5), pages 2675-2691, November.
    21. Abankwah, Stephen Asare & Afriyie, Samuel Osei, 2025. "Modelling Sustainable Energy Transition in BRICS+ Countries: A Smoothed Common Correlated Effects Instrumental Variable Quantile Regression Approach," MPRA Paper 123758, University Library of Munich, Germany.
    22. Lee, Yoonseok & Sul, Donggyu, 2023. "Depth-weighted means of noisy data: An application to estimating the average effect in heterogeneous panels," Journal of Multivariate Analysis, Elsevier, vol. 196(C).
    23. Christis Katsouris, 2023. "Optimal Estimation Methodologies for Panel Data Regression Models," Papers 2311.03471, arXiv.org, revised Nov 2023.
    24. Cepoi, Cosmin-Octavian, 2020. "Asymmetric dependence between stock market returns and news during COVID-19 financial turmoil," Finance Research Letters, Elsevier, vol. 36(C).
    25. Yang, Jisheng & Wei, Jinbao & Cai, Biqing, 2022. "Quantile unit root inference for panel data with common shocks," Economics Letters, Elsevier, vol. 219(C).

    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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